Estimating Structured Vector Autoregressive Models
Authors: Igor Melnyk, Arindam Banerjee
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on synthetic and real data with a variety of structures are presented, validating theoretical results. |
| Researcher Affiliation | Academia | Department of Computer Science and Engineering, University of Minnesota, Twin Cities |
| Pseudocode | No | The paper does not include a pseudocode block or clearly labeled algorithm. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | We used the NASA flight dataset from (nas), consisting of over 100,000 flights, each having a record of about 250 parameters, sampled at 1 Hz. (nas) NASA Aviation Safety Dataset. Available at https://c3.nasa.gov/dashlink/projects/85/. |
| Dataset Splits | Yes | For each flight we separately fitted a first-order VAR model using five approaches and performed 5-fold cross validation to select λ, achieving smallest prediction error. |
| Hardware Specification | No | The paper does not specify the hardware used for running the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., specific libraries, frameworks, or solvers with their versions). |
| Experiment Setup | Yes | To evaluate the estimation problem with L1 norm, we simulated a first-order VAR process for different values of p [10, 600], s [4, 260], and N [10, 5000]. Regularization parameter was varied in the range λN (0, λmax)... For Sparse Group we set α = 0.5, while for OWL the weights c1, . . . , cp were set as a monotonically decreasing sequence. |